Staff Dr. Tim Jahn

Mr. Jahn has left the institute. This page is no longer maintained.

E-Mail: ed tod nnob-inu tod sni ta nhaja tod b@foo tod de

Teaching

Winter semester 2021/22

See teaching activities of the whole group.

Current Research Projects

Publications

Preprints

  1. Convergence of generalized cross-validation for an ill-posed integral equation. T. Jahn. Available as INS Preprint No. 2303, 2023. BibTeX PDF
  2. Discretisation-adaptive regularisation of statistical inverse problems. T. Jahn. Universität Bonn, 2022. BibTeX arxiv

Article

  1. Efficient solution of ill-posed integral equations through averaging. M. Griebel and T. Jahn. IMA Journal of Numerical Analysis, pages draf038, 06 2025. Also available as INS Preprint No. 2401. BibTeX PDF DOI
  2. Regularizing linear inverse problems under unknown non-gaussian white noise allowing repeated measurements. B. Harrach, T. Jahn, and R. Potthast. IMA Journal of Numerical Analysis, 43(1):443–500, 2023. BibTeX arxiv
  3. Noise level free regularization of general linear inverse problems under unconstrained white noise. T. Jahn. SIAM/ASA Journal on Uncertainty Quantification, 11(2):591–615, 2023. BibTeX arxiv
  4. A probabilistic oracle inequality and quantification of uncertainty of a modified discrepancy principle for statistical inverse problems. T. Jahn. Electronic Transactions on Numerical Analysis, 57:35–56, 2022. BibTeX DOI arxiv
  5. Optimal convergence of the discrepancy principle for polynomially and exponentially ill-posed operators under white noise. T. Jahn. Numerical Functional Analysis and Optimization, 43(2):145–167, 2022. BibTeX DOI arXiv arxiv
  6. A modified discrepancy principle to attain optimal convergence rates under unknown noise. T. Jahn. Inverse Problems, 37(9):095008, 2021. BibTeX DOI arxiv
  7. Beyond the bakushinkii veto: regularising linear inverse problems without knowing the noise distribution. B. Harrach, T. Jahn, and R. Potthast. Numerische Mathematik, 145(3):581–603, 2020. BibTeX DOI arxiv
  8. On the discrepancy principle for stochastic gradient descent. T. Jahn and B. Jin. Inverse Problems, 36(9):095009, 2020. BibTeX DOI arxiv
  9. The sensorimotor loop as a dynamical system: how regular motion primitives may emerge from self-organized limit cycles. B. Sándor, T. Jahn, L. Martin, and C. Gros. Frontiers in Robotics and AI, 2:31, 2015. BibTeX DOI

Theses

  1. Regularising linear inverse problems under unknown non-Gaussian noise. T. N. Jahn. PhD thesis, Institute for Mathematics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2020. BibTeX PDF
  2. The high temperature regime of a multi-species mean field spin glass. T. Jahn. Master's thesis, Insititute for Mathematics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2016. BibTeX PDF
  3. Limit cycles of neuron controlled robots in simulated physical environment. T. Jahn. Bachelor's Thesis, Institute for Theoretical Physics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2015. BibTeX PDF